System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork.

ABSTRACT: Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-speci

SUBMITTER: Tanaka Y 

PROVIDER: S-EPMC7072632 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature